Deep Learning for AI Engineers + Interview Preparation

Most deep learning books fail you in one of two ways. The academic ones bury you under dense measure theory, leaving you unable to ship production code. The applied ones hand you a superficial framework wrapper and leave you completely helpless the moment it silently fails at 2:00 a.m

Deep Learning for AI Engineers by Lamhot Siagian is written for the engineer stuck right in the middle. If you can code, want to understand exactly what happens between data and prediction, and need to debug, optimize, and deploy real systems at scale, this is your book

Why This Book is a Must-Buy for Serious Practitioners

• Build Before You Borrow: Remove the magic from AI framework abstractions. You will implement a perceptron, backpropagation, an autograd engine, a CNN, an RNN, and a full GPT completely from scratch in NumPy before you ever call PyTorch or JAX. When you type loss.backward(), you’ll know exactly what it does because you wrote it.

• 100% Real, Runnable, and Verified Code: No idealized textbook outputs. Every single console block is captured verbatim from real scripts. Every from-scratch gradient is numerically checked to ensure it actually executes.
• The Full 9-Stage Lifecycle: A neural network only earns you a seat at the table; the pipeline is how you design the table. Master all nine stages: objective, data, preprocessing, splitting, architecture, training, tuning, evaluation, and production deployment with monitoring.
• 20 Production Case Studies: Learn exactly how industrial titans like Tesla (Autopilot), OpenAI (ChatGPT), YouTube, and DeepMind (AlphaFold 2) realize these systems at scale
• 6 Hands-On Projects: Walk step-by-step through production-grade projects, culminating in building and training your own GPT-style Transformer from scratch.

The 17-Chapter Core Blueprint

1. Part I: Foundations (The Atomic Building Blocks) Master the mathematical and geometric intuition of the perceptron, design vectorized forward passes, derive backpropagation from first principles, and build a micrograd-style autograd engine completely from scratch.
2. Part II: Making Networks Learn (The Practitioner's Toolkit) Go beyond the defaults. Diagnose dead units in activation functions (ReLU, GELU), map complex loss functions, unlock adaptive optimizers (SGD, Momentum, Adam, AdamW), and establish strict weight initialization routines for training stability.
3. Part III: Real Frameworks (Production Engineering)Transition your foundational understanding into production-grade ecosystems. Construct declarative networks in PyTorch, leverage pure functions and JIT compilation in JAX, and deploy an exact debugging decision tree to isolate shape errors and NaN losses.
4. Part IV: Architectures (First-Principles Mastery) Deconstruct the mechanics behind modern AI backbones. Build Convolutional Neural Networks (CNNs), navigate sequential data with Recurrent Gating (LSTM/GRU), and break down the self-attention mechanisms driving modern Transformers and Vision Transformers (ViTs).